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I agree it's illuminating to understand diffusion models in relation to VAEs, but I personally consider them different models, but the line in the sand is defin
by mxwsn 4y ago
I agree it's illuminating to understand diffusion models in relation to VAEs, but I personally consider them different models, but the line in the sand is definitely subjective.
I think this because (reasons I'm sure you're familiar with)
- Diffusion model is closest to a hierarchical VAE, but hierarchical VAEs were significantly less popular than regular VAEs
- The variational objective in diffusion models in practice is weighted
- Diffusion models require unchanging latent dimension while VAEs aren't restricted to this
- Historically, diffusion models grew out of score-based approaches, not from VAEs
- PartiallyTyped 4y agoYou raise good points, if anything, it'd have probably been more accurate of me to express that DDPMs and probabilistic variants fit within the same Bayesian framework as VAEs but with the posterior and likelihood functions simply being markov chains instead. This allows us to separate non probabilistic diffusion models e.g. cold diffusion. But then again, what's the difference between a deterministic model and sampling from a delta function? ;)